A Deep Dive into Machine Learning: The Roles of Neural Networks and Random Forests in QSPR Analysis
Machine learning has significantly improved the field of drug development by enabling the accurate prediction of physicochemical properties and biological activities of compounds. Using machine learning and topological indices to analyze a drug’s structures can make process faster and more accurate....
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| Veröffentlicht in: | BioNanoScience Jg. 15; H. 1 |
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| Format: | Journal Article |
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| Abstract | Machine learning has significantly improved the field of drug development by enabling the accurate prediction of physicochemical properties and biological activities of compounds. Using machine learning and topological indices to analyze a drug’s structures can make process faster and more accurate. Our study explores the molecular characteristics of 15 sulfur-based drugs
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. Topological indices of these drugs have been calculated, and physiochemical properties have been examined using machine learning algorithms. Machine learning algorithms such as artificial neural networks, random forests, and adaptive boosting play a crucial role in this process. These algorithms utilize labeled data to make predictions about intricate molecular activities by assisting in the discovery of novel medication candidates and the enhancement of their properties. These algorithms enhance the accuracy of predictions related to physiochemical properties, reduce the time and cost associated with drug discovery, and rapidly analyze vast datasets by utilizing machine learning, consequently expediting the advancement of novel and efficient therapies. |
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| AbstractList | Machine learning has significantly improved the field of drug development by enabling the accurate prediction of physicochemical properties and biological activities of compounds. Using machine learning and topological indices to analyze a drug’s structures can make process faster and more accurate. Our study explores the molecular characteristics of 15 sulfur-based drugs
(
S
VI
)
. Topological indices of these drugs have been calculated, and physiochemical properties have been examined using machine learning algorithms. Machine learning algorithms such as artificial neural networks, random forests, and adaptive boosting play a crucial role in this process. These algorithms utilize labeled data to make predictions about intricate molecular activities by assisting in the discovery of novel medication candidates and the enhancement of their properties. These algorithms enhance the accuracy of predictions related to physiochemical properties, reduce the time and cost associated with drug discovery, and rapidly analyze vast datasets by utilizing machine learning, consequently expediting the advancement of novel and efficient therapies. |
| ArticleNumber | 89 |
| Author | Ehsan, Huma Ashraf, Tamseela Ahmed, Wakeel AlMutairi, Dalal Ahmed, Shakeel Zaman, Shahid |
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| Cites_doi | 10.1016/S0012-365X(02)00256-X 10.1051/e3sconf/202450804005 10.1021/ja00856a001 10.1038/s41598-023-32347-4 10.1021/ci900115y 10.1039/rr9710400173 10.1038/s41598-024-62819-0 10.1038/s41467-022-35692-6 10.4337/9781803923918.00034 10.1080/10406638.2023.2230336 10.1186/s13321-023-00694-z 10.1201/9780429450532 10.1016/j.ejmech.2018.11.017 10.1039/D1NJ04935F 10.1007/978-3-642-77894-0 10.1080/19361610.2022.2114744 10.1021/ja01193a005 10.21037/atm.2016.06.20 10.1038/s41698-017-0029-7 10.1007/978-1-349-03521-2 10.1142/S0217984924502609 10.1016/j.jocn.2021.07.016 10.21275/ART20203995 10.1002/qua.26594 10.2478/awutm-2013-0014 10.1016/j.chemolab.2022.104690 10.1016/j.amc.2020.125706 10.1016/j.heliyon.2024.e23981 10.13069/jacodesmath.867532 10.1007/s10910-015-0480-z 10.1145/3378936.3378972 10.1038/s41598-023-42340-6 10.1093/bib/bbaa161 10.1016/j.amc.2014.04.091 |
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| Title | A Deep Dive into Machine Learning: The Roles of Neural Networks and Random Forests in QSPR Analysis |
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